Technologies for providing streamlined provisioning of accelerated functions in a disaggregated architecture

ABSTRACT

Technologies for providing streamlined provisioning of accelerated functions in a disaggregated architecture include a compute sled. The compute sled includes a network interface controller and circuitry to determine whether to accelerate a function of a workload executed by the compute sled, and send, to a memory sled and in response to a determination to accelerate the function, a data set on which the function is to operate. The circuitry is also to receive, from the memory sled, a service identifier indicative of a memory location independent handle for data associated with the function, send, to a compute device, a request to schedule acceleration of the function on the data set, receive a notification of completion of the acceleration of the function, and obtain, in response to receipt of the notification and using the service identifier, a resultant data set from the memory sled. The resultant data set was produced by an accelerator device during acceleration of the function on the data set. Other embodiments are also described and claimed.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of Indian Provisional PatentApplication No. 201741030632, filed Aug. 30, 2017 and U.S. ProvisionalPatent Application No. 62/584,401, filed Nov. 10, 2017.

BACKGROUND

In some data centers, a compute device may be equipped with a generalpurpose processor and an accelerator device (e.g., a field programmablegate array (FPGA), an application specific integrated circuit (ASIC), aco-processor, etc.) capable of accelerating a function in a workload(e.g., an application) executed by the compute device. That is, theaccelerator device is connected to the general purpose processor througha local bus (e.g., a Peripheral Component Interconnect express (PCIe)bus). In such systems, the compute device retains exclusive control overthe accelerator device and the accelerator device may go unused forsignificant amounts of time, even when workloads executed by othercompute devices in the data center could benefit from acceleration. Inother systems, accelerator devices may be disaggregated from (e.g.,physically separated from) a compute device that is executing aworkload, but can be selectively allocated for use by the compute devicethrough a network connection. In some instances, an intermediary computedevice may coordinate the allocation of disaggregated acceleratordevices to compute devices on an as-requested basis, and, in doing so,may receive and send data between the devices. As the number of computedevices and accelerator devices the data center increases, thecommunication of data to and from the intermediary compute deviceincreases accordingly, and may result in network congestion and latencythat may diminish any speed increases that would otherwise be obtainedfrom accelerating portions of workloads.

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and notby way of limitation in the accompanying figures. For simplicity andclarity of illustration, elements illustrated in the figures are notnecessarily drawn to scale. Where considered appropriate, referencelabels have been repeated among the figures to indicate corresponding oranalogous elements.

FIG. 1 is a simplified diagram of at least one embodiment of a datacenter for executing workloads with disaggregated resources;

FIG. 2 is a simplified diagram of at least one embodiment of a pod ofthe data center of FIG. 1;

FIG. 3 is a perspective view of at least one embodiment of a rack thatmay be included in the pod of FIG. 2;

FIG. 4 is a side plan elevation view of the rack of FIG. 3;

FIG. 5 is a perspective view of the rack of FIG. 3 having a sled mountedtherein;

FIG. 6 is a is a simplified block diagram of at least one embodiment ofa top side of the sled of FIG. 5;

FIG. 7 is a simplified block diagram of at least one embodiment of abottom side of the sled of FIG. 6;

FIG. 8 is a simplified block diagram of at least one embodiment of acompute sled usable in the data center of FIG. 1;

FIG. 9 is a top perspective view of at least one embodiment of thecompute sled of FIG. 8;

FIG. 10 is a simplified block diagram of at least one embodiment of anaccelerator sled usable in the data center of FIG. 1;

FIG. 11 is a top perspective view of at least one embodiment of theaccelerator sled of FIG. 10;

FIG. 12 is a simplified block diagram of at least one embodiment of astorage sled usable in the data center of FIG. 1;

FIG. 13 is a top perspective view of at least one embodiment of thestorage sled of FIG. 12;

FIG. 14 is a simplified block diagram of at least one embodiment of amemory sled usable in the data center of FIG. 1; and

FIG. 15 is a simplified block diagram of a system that may beestablished within the data center of FIG. 1 to execute workloads withmanaged nodes composed of disaggregated resources.

FIG. 16 is a simplified block diagram of at least one embodiment of asystem for providing streamlined provisioning of accelerated functions;

FIG. 17 is a simplified block diagram of at least one embodiment of amethod for utilizing streamlined provisioning of accelerated functionsthat may be performed by a compute sled of FIG. 16;

FIG. 18 is a simplified block diagram of at least one embodiment of amethod for facilitating streamlined provisioning of acceleratedfunctions that may be performed by a memory sled of FIG. 16;

FIG. 19 is a simplified block diagram of at least one embodiment of amethod for accelerating a function that may be performed by anaccelerator sled of FIG. 16; and

FIGS. 20-21 are a simplified flow diagram of at least one embodiment ofa method for coordinating the provisioning of accelerated functions as aservice that may be performed by a pod manager of FIG. 16.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof havebeen shown by way of example in the drawings and will be describedherein in detail. It should be understood, however, that there is nointent to limit the concepts of the present disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives consistent with the presentdisclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,”“an illustrative embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may or may not necessarily includethat particular feature, structure, or characteristic. Moreover, suchphrases are not necessarily referring to the same embodiment. Further,when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to effect such feature, structure,or characteristic in connection with other embodiments whether or notexplicitly described. Additionally, it should be appreciated that itemsincluded in a list in the form of “at least one A, B, and C” can mean(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).Similarly, items listed in the form of “at least one of A, B, or C” canmean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

The disclosed embodiments may be implemented, in some cases, inhardware, firmware, software, or any combination thereof. The disclosedembodiments may also be implemented as instructions carried by or storedon a transitory or non-transitory machine-readable (e.g.,computer-readable) storage medium, which may be read and executed by oneor more processors. A machine-readable storage medium may be embodied asany storage device, mechanism, or other physical structure for storingor transmitting information in a form readable by a machine (e.g., avolatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown inspecific arrangements and/or orderings. However, it should beappreciated that such specific arrangements and/or orderings may not berequired. Rather, in some embodiments, such features may be arranged ina different manner and/or order than shown in the illustrative figures.Additionally, the inclusion of a structural or method feature in aparticular figure is not meant to imply that such feature is required inall embodiments and, in some embodiments, may not be included or may becombined with other features.

Referring now to FIG. 1, a data center 100 in which disaggregatedresources may cooperatively execute one or more workloads (e.g.,applications on behalf of customers) includes multiple pods 110, 120,130, 140, each of which includes one or more rows of racks. As describedin more detail herein, each rack houses multiple sleds, which each maybe embodied as a compute device, such as a server, that is primarilyequipped with a particular type of resource (e.g., memory devices, datastorage devices, accelerator devices, general purpose processors). Inthe illustrative embodiment, the sleds in each pod 110, 120, 130, 140are connected to multiple pod switches (e.g., switches that route datacommunications to and from sleds within the pod). The pod switches, inturn, connect with spine switches 150 that switch communications amongpods (e.g., the pods 110, 120, 130, 140) in the data center 100. In someembodiments, the sleds may be connected with a fabric using IntelOmni-Path technology. As described in more detail herein, resourceswithin sleds in the data center 100 may be allocated to a group(referred to herein as a “managed node”) containing resources from oneor more other sleds to be collectively utilized in the execution of aworkload. The workload can execute as if the resources belonging to themanaged node were located on the same sled. The resources in a managednode may even belong to sleds belonging to different racks, and even todifferent pods 110, 120, 130, 140. Some resources of a single sled maybe allocated to one managed node while other resources of the same sledare allocated to a different managed node (e.g., one processor assignedto one managed node and another processor of the same sled assigned to adifferent managed node). By disaggregating resources to sleds comprisedpredominantly of a single type of resource (e.g., compute sledscomprising primarily compute resources, memory sleds containingprimarily memory resources), and selectively allocating and deallocatingthe disaggregated resources to form a managed node assigned to execute aworkload, the data center 100 provides more efficient resource usageover typical data centers comprised of hyperconverged servers containingcompute, memory, storage and perhaps additional resources). As such, thedata center 100 may provide greater performance (e.g., throughput,operations per second, latency, etc.) than a typical data center thathas the same number of resources.

Referring now to FIG. 2, the pod 110, in the illustrative embodiment,includes a set of rows 200, 210, 220, 230 of racks 240. Each rack 240may house multiple sleds (e.g., sixteen sleds) and provide power anddata connections to the housed sleds, as described in more detailherein. In the illustrative embodiment, the racks in each row 200, 210,220, 230 are connected to multiple pod switches 250, 260. The pod switch250 includes a set of ports 252 to which the sleds of the racks of thepod 110 are connected and another set of ports 254 that connect the pod110 to the spine switches 150 to provide connectivity to other pods inthe data center 100. Similarly, the pod switch 260 includes a set ofports 262 to which the sleds of the racks of the pod 110 are connectedand a set of ports 264 that connect the pod 110 to the spine switches150. As such, the use of the pair of switches 250, 260 provides anamount of redundancy to the pod 110. For example, if either of theswitches 250, 260 fails, the sleds in the pod 110 may still maintaindata communication with the remainder of the data center 100 (e.g.,sleds of other pods) through the other switch 250, 260. Furthermore, inthe illustrative embodiment, the switches 150, 250, 260 may be embodiedas dual-mode optical switches, capable of routing both Ethernet protocolcommunications carrying Internet Protocol (IP) packets andcommunications according to a second, high-performance link-layerprotocol (e.g., Intel's Omni-Path Architecture's, Infiniband) viaoptical signaling media of an optical fabric.

It should be appreciated that each of the other pods 120, 130, 140 (aswell as any additional pods of the data center 100) may be similarlystructured as, and have components similar to, the pod 110 shown in anddescribed in regard to FIG. 2 (e.g., each pod may have rows of rackshousing multiple sleds as described above). Additionally, while two podswitches 250, 260 are shown, it should be understood that in otherembodiments, each pod 110, 120, 130, 140 may be connected to differentnumber of pod switches (e.g., providing even more failover capacity).

Referring now to FIGS. 3-5, each illustrative rack 240 of the datacenter 100 includes two elongated support posts 302, 304, which arearranged vertically. For example, the elongated support posts 302, 304may extend upwardly from a floor of the data center 100 when deployed.The rack 240 also includes one or more horizontal pairs 310 of elongatedsupport arms 312 (identified in FIG. 3 via a dashed ellipse) configuredto support a sled of the data center 100 as discussed below. Oneelongated support arm 312 of the pair of elongated support arms 312extends outwardly from the elongated support post 302 and the otherelongated support arm 312 extends outwardly from the elongated supportpost 304.

In the illustrative embodiments, each sled of the data center 100 isembodied as a chassis-less sled. That is, each sled has a chassis-lesscircuit board substrate on which physical resources (e.g., processors,memory, accelerators, storage, etc.) are mounted as discussed in moredetail below. As such, the rack 240 is configured to receive thechassis-less sleds. For example, each pair 310 of elongated support arms312 defines a sled slot 320 of the rack 240, which is configured toreceive a corresponding chassis-less sled. To do so, each illustrativeelongated support arm 312 includes a circuit board guide 330 configuredto receive the chassis-less circuit board substrate of the sled. Eachcircuit board guide 330 is secured to, or otherwise mounted to, a topside 332 of the corresponding elongated support arm 312. For example, inthe illustrative embodiment, each circuit board guide 330 is mounted ata distal end of the corresponding elongated support arm 312 relative tothe corresponding elongated support post 302, 304. For clarity of theFigures, not every circuit board guide 330 may be referenced in eachFigure.

Each circuit board guide 330 includes an inner wall that defines acircuit board slot 380 configured to receive the chassis-less circuitboard substrate of a sled 400 when the sled 400 is received in thecorresponding sled slot 320 of the rack 240. To do so, as shown in FIG.4, a user (or robot) aligns the chassis-less circuit board substrate ofan illustrative chassis-less sled 400 to a sled slot 320. The user, orrobot, may then slide the chassis-less circuit board substrate forwardinto the sled slot 320 such that each side edge 414 of the chassis-lesscircuit board substrate is received in a corresponding circuit boardslot 380 of the circuit board guides 330 of the pair 310 of elongatedsupport arms 312 that define the corresponding sled slot 320 as shown inFIG. 4. By having robotically accessible and robotically manipulablesleds comprising disaggregated resources, each type of resource can beupgraded independently of each other and at their own optimized refreshrate. Furthermore, the sleds are configured to blindly mate with powerand data communication cables in each rack 240, enhancing their abilityto be quickly removed, upgraded, reinstalled, and/or replaced. As such,in some embodiments, the data center 100 may operate (e.g., executeworkloads, undergo maintenance and/or upgrades, etc.) without humaninvolvement on the data center floor. In other embodiments, a human mayfacilitate one or more maintenance or upgrade operations in the datacenter 100.

It should be appreciated that each circuit board guide 330 is dualsided. That is, each circuit board guide 330 includes an inner wall thatdefines a circuit board slot 380 on each side of the circuit board guide330. In this way, each circuit board guide 330 can support achassis-less circuit board substrate on either side. As such, a singleadditional elongated support post may be added to the rack 240 to turnthe rack 240 into a two-rack solution that can hold twice as many sledslots 320 as shown in FIG. 3. The illustrative rack 240 includes sevenpairs 310 of elongated support arms 312 that define a correspondingseven sled slots 320, each configured to receive and support acorresponding sled 400 as discussed above. Of course, in otherembodiments, the rack 240 may include additional or fewer pairs 310 ofelongated support arms 312 (i.e., additional or fewer sled slots 320).It should be appreciated that because the sled 400 is chassis-less, thesled 400 may have an overall height that is different than typicalservers. As such, in some embodiments, the height of each sled slot 320may be shorter than the height of a typical server (e.g., shorter than asingle rank unit, “1U”). That is, the vertical distance between eachpair 310 of elongated support arms 312 may be less than a standard rackunit “1U.” Additionally, due to the relative decrease in height of thesled slots 320, the overall height of the rack 240 in some embodimentsmay be shorter than the height of traditional rack enclosures. Forexample, in some embodiments, each of the elongated support posts 302,304 may have a length of six feet or less. Again, in other embodiments,the rack 240 may have different dimensions. Further, it should beappreciated that the rack 240 does not include any walls, enclosures, orthe like. Rather, the rack 240 is an enclosure-less rack that is openedto the local environment. Of course, in some cases, an end plate may beattached to one of the elongated support posts 302, 304 in thosesituations in which the rack 240 forms an end-of-row rack in the datacenter 100.

In some embodiments, various interconnects may be routed upwardly ordownwardly through the elongated support posts 302, 304. To facilitatesuch routing, each elongated support post 302, 304 includes an innerwall that defines an inner chamber in which the interconnect may belocated. The interconnects routed through the elongated support posts302, 304 may be embodied as any type of interconnects including, but notlimited to, data or communication interconnects to provide communicationconnections to each sled slot 320, power interconnects to provide powerto each sled slot 320, and/or other types of interconnects.

The rack 240, in the illustrative embodiment, includes a supportplatform on which a corresponding optical data connector (not shown) ismounted. Each optical data connector is associated with a correspondingsled slot 320 and is configured to mate with an optical data connectorof a corresponding sled 400 when the sled 400 is received in thecorresponding sled slot 320. In some embodiments, optical connectionsbetween components (e.g., sleds, racks, and switches) in the data center100 are made with a blind mate optical connection. For example, a dooron each cable may prevent dust from contaminating the fiber inside thecable. In the process of connecting to a blind mate optical connectormechanism, the door is pushed open when the end of the cable enters theconnector mechanism. Subsequently, the optical fiber inside the cableenters a gel within the connector mechanism and the optical fiber of onecable comes into contact with the optical fiber of another cable withinthe gel inside the connector mechanism.

The illustrative rack 240 also includes a fan array 370 coupled to thecross-support arms of the rack 240. The fan array 370 includes one ormore rows of cooling fans 372, which are aligned in a horizontal linebetween the elongated support posts 302, 304. In the illustrativeembodiment, the fan array 370 includes a row of cooling fans 372 foreach sled slot 320 of the rack 240. As discussed above, each sled 400does not include any on-board cooling system in the illustrativeembodiment and, as such, the fan array 370 provides cooling for eachsled 400 received in the rack 240. Each rack 240, in the illustrativeembodiment, also includes a power supply associated with each sled slot320. Each power supply is secured to one of the elongated support arms312 of the pair 310 of elongated support arms 312 that define thecorresponding sled slot 320. For example, the rack 240 may include apower supply coupled or secured to each elongated support arm 312extending from the elongated support post 302. Each power supplyincludes a power connector configured to mate with a power connector ofthe sled 400 when the sled 400 is received in the corresponding sledslot 320. In the illustrative embodiment, the sled 400 does not includeany on-board power supply and, as such, the power supplies provided inthe rack 240 supply power to corresponding sleds 400 when mounted to therack 240.

Referring now to FIG. 6, the sled 400, in the illustrative embodiment,is configured to be mounted in a corresponding rack 240 of the datacenter 100 as discussed above. In some embodiments, each sled 400 may beoptimized or otherwise configured for performing particular tasks, suchas compute tasks, acceleration tasks, data storage tasks, etc. Forexample, the sled 400 may be embodied as a compute sled 800 as discussedbelow in regard to FIGS. 8-9, an accelerator sled 1000 as discussedbelow in regard to FIGS. 10-11, a storage sled 1200 as discussed belowin regard to FIGS. 12-13, or as a sled optimized or otherwise configuredto perform other specialized tasks, such as a memory sled 1400,discussed below in regard to FIG. 14.

As discussed above, the illustrative sled 400 includes a chassis-lesscircuit board substrate 602, which supports various physical resources(e.g., electrical components) mounted thereon. It should be appreciatedthat the circuit board substrate 602 is “chassis-less” in that the sled400 does not include a housing or enclosure. Rather, the chassis-lesscircuit board substrate 602 is open to the local environment. Thechassis-less circuit board substrate 602 may be formed from any materialcapable of supporting the various electrical components mounted thereon.For example, in an illustrative embodiment, the chassis-less circuitboard substrate 602 is formed from an FR-4 glass-reinforced epoxylaminate material. Of course, other materials may be used to form thechassis-less circuit board substrate 602 in other embodiments.

As discussed in more detail below, the chassis-less circuit boardsubstrate 602 includes multiple features that improve the thermalcooling characteristics of the various electrical components mounted onthe chassis-less circuit board substrate 602. As discussed, thechassis-less circuit board substrate 602 does not include a housing orenclosure, which may improve the airflow over the electrical componentsof the sled 400 by reducing those structures that may inhibit air flow.For example, because the chassis-less circuit board substrate 602 is notpositioned in an individual housing or enclosure, there is no backplane(e.g., a backplate of the chassis) to the chassis-less circuit boardsubstrate 602, which could inhibit air flow across the electricalcomponents. Additionally, the chassis-less circuit board substrate 602has a geometric shape configured to reduce the length of the airflowpath across the electrical components mounted to the chassis-lesscircuit board substrate 602. For example, the illustrative chassis-lesscircuit board substrate 602 has a width 604 that is greater than a depth606 of the chassis-less circuit board substrate 602. In one particularembodiment, for example, the chassis-less circuit board substrate 602has a width of about 21 inches and a depth of about 9 inches, comparedto a typical server that has a width of about 17 inches and a depth ofabout 39 inches. As such, an airflow path 608 that extends from a frontedge 610 of the chassis-less circuit board substrate 602 toward a rearedge 612 has a shorter distance relative to typical servers, which mayimprove the thermal cooling characteristics of the sled 400.Furthermore, although not illustrated in FIG. 6, the various physicalresources mounted to the chassis-less circuit board substrate 602 aremounted in corresponding locations such that no two substantivelyheat-producing electrical components shadow each other as discussed inmore detail below. That is, no two electrical components, which produceappreciable heat during operation (i.e., greater than a nominal heatsufficient enough to adversely impact the cooling of another electricalcomponent), are mounted to the chassis-less circuit board substrate 602linearly in-line with each other along the direction of the airflow path608 (i.e., along a direction extending from the front edge 610 towardthe rear edge 612 of the chassis-less circuit board substrate 602).

As discussed above, the illustrative sled 400 includes one or morephysical resources 620 mounted to a top side 650 of the chassis-lesscircuit board substrate 602. Although two physical resources 620 areshown in FIG. 6, it should be appreciated that the sled 400 may includeone, two, or more physical resources 620 in other embodiments. Thephysical resources 620 may be embodied as any type of processor,controller, or other compute circuit capable of performing various taskssuch as compute functions and/or controlling the functions of the sled400 depending on, for example, the type or intended functionality of thesled 400. For example, as discussed in more detail below, the physicalresources 620 may be embodied as high-performance processors inembodiments in which the sled 400 is embodied as a compute sled, asaccelerator co-processors or circuits in embodiments in which the sled400 is embodied as an accelerator sled, storage controllers inembodiments in which the sled 400 is embodied as a storage sled, or aset of memory devices in embodiments in which the sled 400 is embodiedas a memory sled.

The sled 400 also includes one or more additional physical resources 630mounted to the top side 650 of the chassis-less circuit board substrate602. In the illustrative embodiment, the additional physical resourcesinclude a network interface controller (NIC) as discussed in more detailbelow. Of course, depending on the type and functionality of the sled400, the physical resources 630 may include additional or otherelectrical components, circuits, and/or devices in other embodiments.

The physical resources 620 are communicatively coupled to the physicalresources 630 via an input/output (I/O) subsystem 622. The I/O subsystem622 may be embodied as circuitry and/or components to facilitateinput/output operations with the physical resources 620, the physicalresources 630, and/or other components of the sled 400. For example, theI/O subsystem 622 may be embodied as, or otherwise include, memorycontroller hubs, input/output control hubs, integrated sensor hubs,firmware devices, communication links (e.g., point-to-point links, buslinks, wires, cables, light guides, printed circuit board traces, etc.),and/or other components and subsystems to facilitate the input/outputoperations. In the illustrative embodiment, the I/O subsystem 622 isembodied as, or otherwise includes, a double data rate 4 (DDR4) data busor a DDR5 data bus.

In some embodiments, the sled 400 may also include aresource-to-resource interconnect 624. The resource-to-resourceinterconnect 624 may be embodied as any type of communicationinterconnect capable of facilitating resource-to-resourcecommunications. In the illustrative embodiment, the resource-to-resourceinterconnect 624 is embodied as a high-speed point-to-point interconnect(e.g., faster than the I/O subsystem 622). For example, theresource-to-resource interconnect 624 may be embodied as a QuickPathInterconnect (QPI), an UltraPath Interconnect (UPI), or other high-speedpoint-to-point interconnect dedicated to resource-to-resourcecommunications.

The sled 400 also includes a power connector 640 configured to mate witha corresponding power connector of the rack 240 when the sled 400 ismounted in the corresponding rack 240. The sled 400 receives power froma power supply of the rack 240 via the power connector 640 to supplypower to the various electrical components of the sled 400. That is, thesled 400 does not include any local power supply (i.e., an on-boardpower supply) to provide power to the electrical components of the sled400. The exclusion of a local or on-board power supply facilitates thereduction in the overall footprint of the chassis-less circuit boardsubstrate 602, which may increase the thermal cooling characteristics ofthe various electrical components mounted on the chassis-less circuitboard substrate 602 as discussed above. In some embodiments, power isprovided to the processors 820 through vias directly under theprocessors 820 (e.g., through the bottom side 750 of the chassis-lesscircuit board substrate 602), providing an increased thermal budget,additional current and/or voltage, and better voltage control overtypical boards.

In some embodiments, the sled 400 may also include mounting features 642configured to mate with a mounting arm, or other structure, of a robotto facilitate the placement of the sled 600 in a rack 240 by the robot.The mounting features 642 may be embodied as any type of physicalstructures that allow the robot to grasp the sled 400 without damagingthe chassis-less circuit board substrate 602 or the electricalcomponents mounted thereto. For example, in some embodiments, themounting features 642 may be embodied as non-conductive pads attached tothe chassis-less circuit board substrate 602. In other embodiments, themounting features may be embodied as brackets, braces, or other similarstructures attached to the chassis-less circuit board substrate 602. Theparticular number, shape, size, and/or make-up of the mounting feature642 may depend on the design of the robot configured to manage the sled400.

Referring now to FIG. 7, in addition to the physical resources 630mounted on the top side 650 of the chassis-less circuit board substrate602, the sled 400 also includes one or more memory devices 720 mountedto a bottom side 750 of the chassis-less circuit board substrate 602.That is, the chassis-less circuit board substrate 602 is embodied as adouble-sided circuit board. The physical resources 620 arecommunicatively coupled to the memory devices 720 via the I/O subsystem622. For example, the physical resources 620 and the memory devices 720may be communicatively coupled by one or more vias extending through thechassis-less circuit board substrate 602. Each physical resource 620 maybe communicatively coupled to a different set of one or more memorydevices 720 in some embodiments. Alternatively, in other embodiments,each physical resource 620 may be communicatively coupled to each memorydevices 720.

The memory devices 720 may be embodied as any type of memory devicecapable of storing data for the physical resources 620 during operationof the sled 400, such as any type of volatile (e.g., dynamic randomaccess memory (DRAM), etc.) or non-volatile memory. Volatile memory maybe a storage medium that requires power to maintain the state of datastored by the medium. Non-limiting examples of volatile memory mayinclude various types of random access memory (RAM), such as dynamicrandom access memory (DRAM) or static random access memory (SRAM). Oneparticular type of DRAM that may be used in a memory module issynchronous dynamic random access memory (SDRAM). In particularembodiments, DRAM of a memory component may comply with a standardpromulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 forLow Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, andJESD209-4 for LPDDR4 (these standards are available at www.jedec.org).Such standards (and similar standards) may be referred to as DDR-basedstandards and communication interfaces of the storage devices thatimplement such standards may be referred to as DDR-based interfaces.

In one embodiment, the memory device is a block addressable memorydevice, such as those based on NAND or NOR technologies. A memory devicemay also include next-generation nonvolatile devices, such as Intel 3DXPoint™ memory or other byte addressable write-in-place nonvolatilememory devices. In one embodiment, the memory device may be or mayinclude memory devices that use chalcogenide glass, multi-thresholdlevel NAND flash memory, NOR flash memory, single or multi-level PhaseChange Memory (PCM), a resistive memory, nanowire memory, ferroelectrictransistor random access memory (FeTRAM), anti-ferroelectric memory,magnetoresistive random access memory (MRAM) memory that incorporatesmemristor technology, resistive memory including the metal oxide base,the oxygen vacancy base and the conductive bridge Random Access Memory(CB-RAM), or spin transfer torque (STT)-MRAM, a spintronic magneticjunction memory based device, a magnetic tunneling junction (MTJ) baseddevice, a DW (Domain Wall) and SOT (Spin Orbit Transfer) based device, athyristor based memory device, or a combination of any of the above, orother memory. The memory device may refer to the die itself and/or to apackaged memory product. In some embodiments, the memory device maycomprise a transistor-less stackable cross point architecture in whichmemory cells sit at the intersection of word lines and bit lines and areindividually addressable and in which bit storage is based on a changein bulk resistance.

Referring now to FIG. 8, in some embodiments, the sled 400 may beembodied as a compute sled 800. The compute sled 800 is optimized, orotherwise configured, to perform compute tasks. Of course, as discussedabove, the compute sled 800 may rely on other sleds, such asacceleration sleds and/or storage sleds, to perform such compute tasks.The compute sled 800 includes various physical resources (e.g.,electrical components) similar to the physical resources of the sled400, which have been identified in FIG. 8 using the same referencenumbers. The description of such components provided above in regard toFIGS. 6 and 7 applies to the corresponding components of the computesled 800 and is not repeated herein for clarity of the description ofthe compute sled 800.

In the illustrative compute sled 800, the physical resources 620 areembodied as processors 820. Although only two processors 820 are shownin FIG. 8, it should be appreciated that the compute sled 800 mayinclude additional processors 820 in other embodiments. Illustratively,the processors 820 are embodied as high-performance processors 820 andmay be configured to operate at a relatively high power rating. Althoughthe processors 820 generate additional heat operating at power ratingsgreater than typical processors (which operate at around 155-230 W), theenhanced thermal cooling characteristics of the chassis-less circuitboard substrate 602 discussed above facilitate the higher poweroperation. For example, in the illustrative embodiment, the processors820 are configured to operate at a power rating of at least 250 W. Insome embodiments, the processors 820 may be configured to operate at apower rating of at least 350 W.

In some embodiments, the compute sled 800 may also include aprocessor-to-processor interconnect 842. Similar to theresource-to-resource interconnect 624 of the sled 400 discussed above,the processor-to-processor interconnect 842 may be embodied as any typeof communication interconnect capable of facilitatingprocessor-to-processor interconnect 842 communications. In theillustrative embodiment, the processor-to-processor interconnect 842 isembodied as a high-speed point-to-point interconnect (e.g., faster thanthe I/O subsystem 622). For example, the processor-to-processorinterconnect 842 may be embodied as a QuickPath Interconnect (QPI), anUltraPath Interconnect (UPI), or other high-speed point-to-pointinterconnect dedicated to processor-to-processor communications.

The compute sled 800 also includes a communication circuit 830. Theillustrative communication circuit 830 includes a network interfacecontroller (NIC) 832, which may also be referred to as a host fabricinterface (HFI). The NIC 832 may be embodied as, or otherwise include,any type of integrated circuit, discrete circuits, controller chips,chipsets, add-in-boards, daughtercards, network interface cards, otherdevices that may be used by the compute sled 800 to connect with anothercompute device (e.g., with other sleds 400). In some embodiments, theNIC 832 may be embodied as part of a system-on-a-chip (SoC) thatincludes one or more processors, or included on a multichip package thatalso contains one or more processors. In some embodiments, the NIC 832may include a local processor (not shown) and/or a local memory (notshown) that are both local to the NIC 832. In such embodiments, thelocal processor of the NIC 832 may be capable of performing one or moreof the functions of the processors 820. Additionally or alternatively,in such embodiments, the local memory of the NIC 832 may be integratedinto one or more components of the compute sled at the board level,socket level, chip level, and/or other levels.

The communication circuit 830 is communicatively coupled to an opticaldata connector 834. The optical data connector 834 is configured to matewith a corresponding optical data connector of the rack 240 when thecompute sled 800 is mounted in the rack 240. Illustratively, the opticaldata connector 834 includes a plurality of optical fibers which leadfrom a mating surface of the optical data connector 834 to an opticaltransceiver 836. The optical transceiver 836 is configured to convertincoming optical signals from the rack-side optical data connector toelectrical signals and to convert electrical signals to outgoing opticalsignals to the rack-side optical data connector. Although shown asforming part of the optical data connector 834 in the illustrativeembodiment, the optical transceiver 836 may form a portion of thecommunication circuit 830 in other embodiments.

In some embodiments, the compute sled 800 may also include an expansionconnector 840. In such embodiments, the expansion connector 840 isconfigured to mate with a corresponding connector of an expansionchassis-less circuit board substrate to provide additional physicalresources to the compute sled 800. The additional physical resources maybe used, for example, by the processors 820 during operation of thecompute sled 800. The expansion chassis-less circuit board substrate maybe substantially similar to the chassis-less circuit board substrate 602discussed above and may include various electrical components mountedthereto. The particular electrical components mounted to the expansionchassis-less circuit board substrate may depend on the intendedfunctionality of the expansion chassis-less circuit board substrate. Forexample, the expansion chassis-less circuit board substrate may provideadditional compute resources, memory resources, and/or storageresources. As such, the additional physical resources of the expansionchassis-less circuit board substrate may include, but is not limited to,processors, memory devices, storage devices, and/or accelerator circuitsincluding, for example, field programmable gate arrays (FPGA),application-specific integrated circuits (ASICs), securityco-processors, graphics processing units (GPUs), machine learningcircuits, or other specialized processors, controllers, devices, and/orcircuits.

Referring now to FIG. 9, an illustrative embodiment of the compute sled800 is shown. As shown, the processors 820, communication circuit 830,and optical data connector 834 are mounted to the top side 650 of thechassis-less circuit board substrate 602. Any suitable attachment ormounting technology may be used to mount the physical resources of thecompute sled 800 to the chassis-less circuit board substrate 602. Forexample, the various physical resources may be mounted in correspondingsockets (e.g., a processor socket), holders, or brackets. In some cases,some of the electrical components may be directly mounted to thechassis-less circuit board substrate 602 via soldering or similartechniques.

As discussed above, the individual processors 820 and communicationcircuit 830 are mounted to the top side 650 of the chassis-less circuitboard substrate 602 such that no two heat-producing, electricalcomponents shadow each other. In the illustrative embodiment, theprocessors 820 and communication circuit 830 are mounted incorresponding locations on the top side 650 of the chassis-less circuitboard substrate 602 such that no two of those physical resources arelinearly in-line with others along the direction of the airflow path608. It should be appreciated that, although the optical data connector834 is in-line with the communication circuit 830, the optical dataconnector 834 produces no or nominal heat during operation.

The memory devices 720 of the compute sled 800 are mounted to the bottomside 750 of the of the chassis-less circuit board substrate 602 asdiscussed above in regard to the sled 400. Although mounted to thebottom side 750, the memory devices 720 are communicatively coupled tothe processors 820 located on the top side 650 via the I/O subsystem622. Because the chassis-less circuit board substrate 602 is embodied asa double-sided circuit board, the memory devices 720 and the processors820 may be communicatively coupled by one or more vias, connectors, orother mechanisms extending through the chassis-less circuit boardsubstrate 602. Of course, each processor 820 may be communicativelycoupled to a different set of one or more memory devices 720 in someembodiments. Alternatively, in other embodiments, each processor 820 maybe communicatively coupled to each memory device 720. In someembodiments, the memory devices 720 may be mounted to one or more memorymezzanines on the bottom side of the chassis-less circuit boardsubstrate 602 and may interconnect with a corresponding processor 820through a ball-grid array.

Each of the processors 820 includes a heatsink 850 secured thereto. Dueto the mounting of the memory devices 720 to the bottom side 750 of thechassis-less circuit board substrate 602 (as well as the verticalspacing of the sleds 400 in the corresponding rack 240), the top side650 of the chassis-less circuit board substrate 602 includes additional“free” area or space that facilitates the use of heatsinks 850 having alarger size relative to traditional heatsinks used in typical servers.Additionally, due to the improved thermal cooling characteristics of thechassis-less circuit board substrate 602, none of the processorheatsinks 850 include cooling fans attached thereto. That is, each ofthe heatsinks 850 is embodied as a fan-less heatsinks.

Referring now to FIG. 10, in some embodiments, the sled 400 may beembodied as an accelerator sled 1000. The accelerator sled 1000 isoptimized, or otherwise configured, to perform specialized computetasks, such as machine learning, encryption, hashing, or othercomputational-intensive task. In some embodiments, for example, acompute sled 800 may offload tasks to the accelerator sled 1000 duringoperation. The accelerator sled 1000 includes various components similarto components of the sled 400 and/or compute sled 800, which have beenidentified in FIG. 10 using the same reference numbers. The descriptionof such components provided above in regard to FIGS. 6, 7, and 8 applyto the corresponding components of the accelerator sled 1000 and is notrepeated herein for clarity of the description of the accelerator sled1000.

In the illustrative accelerator sled 1000, the physical resources 620are embodied as accelerator circuits 1020. Although only two acceleratorcircuits 1020 are shown in FIG. 10, it should be appreciated that theaccelerator sled 1000 may include additional accelerator circuits 1020in other embodiments. For example, as shown in FIG. 11, the acceleratorsled 1000 may include four accelerator circuits 1020 in someembodiments. The accelerator circuits 1020 may be embodied as any typeof processor, co-processor, compute circuit, or other device capable ofperforming compute or processing operations. For example, theaccelerator circuits 1020 may be embodied as, for example, fieldprogrammable gate arrays (FPGA), application-specific integratedcircuits (ASICs), security co-processors, graphics processing units(GPUs), machine learning circuits, or other specialized processors,controllers, devices, and/or circuits.

In some embodiments, the accelerator sled 1000 may also include anaccelerator-to-accelerator interconnect 1042. Similar to theresource-to-resource interconnect 624 of the sled 600 discussed above,the accelerator-to-accelerator interconnect 1042 may be embodied as anytype of communication interconnect capable of facilitatingaccelerator-to-accelerator communications. In the illustrativeembodiment, the accelerator-to-accelerator interconnect 1042 is embodiedas a high-speed point-to-point interconnect (e.g., faster than the I/Osubsystem 622). For example, the accelerator-to-accelerator interconnect1042 may be embodied as a QuickPath Interconnect (QPI), an UltraPathInterconnect (UPI), or other high-speed point-to-point interconnectdedicated to processor-to-processor communications. In some embodiments,the accelerator circuits 1020 may be daisy-chained with a primaryaccelerator circuit 1020 connected to the NIC 832 and memory 720 throughthe I/O subsystem 622 and a secondary accelerator circuit 1020 connectedto the NIC 832 and memory 720 through a primary accelerator circuit1020.

Referring now to FIG. 11, an illustrative embodiment of the acceleratorsled 1000 is shown. As discussed above, the accelerator circuits 1020,communication circuit 830, and optical data connector 834 are mounted tothe top side 650 of the chassis-less circuit board substrate 602. Again,the individual accelerator circuits 1020 and communication circuit 830are mounted to the top side 650 of the chassis-less circuit boardsubstrate 602 such that no two heat-producing, electrical componentsshadow each other as discussed above. The memory devices 720 of theaccelerator sled 1000 are mounted to the bottom side 750 of the of thechassis-less circuit board substrate 602 as discussed above in regard tothe sled 600. Although mounted to the bottom side 750, the memorydevices 720 are communicatively coupled to the accelerator circuits 1020located on the top side 650 via the I/O subsystem 622 (e.g., throughvias). Further, each of the accelerator circuits 1020 may include aheatsink 1070 that is larger than a traditional heatsink used in aserver. As discussed above with reference to the heatsinks 870, theheatsinks 1070 may be larger than tradition heatsinks because of the“free” area provided by the memory devices 750 being located on thebottom side 750 of the chassis-less circuit board substrate 602 ratherthan on the top side 650.

Referring now to FIG. 12, in some embodiments, the sled 400 may beembodied as a storage sled 1200. The storage sled 1200 is optimized, orotherwise configured, to store data in a data storage 1250 local to thestorage sled 1200. For example, during operation, a compute sled 800 oran accelerator sled 1000 may store and retrieve data from the datastorage 1250 of the storage sled 1200. The storage sled 1200 includesvarious components similar to components of the sled 400 and/or thecompute sled 800, which have been identified in FIG. 12 using the samereference numbers. The description of such components provided above inregard to FIGS. 6, 7, and 8 apply to the corresponding components of thestorage sled 1200 and is not repeated herein for clarity of thedescription of the storage sled 1200.

In the illustrative storage sled 1200, the physical resources 620 areembodied as storage controllers 1220. Although only two storagecontrollers 1220 are shown in FIG. 12, it should be appreciated that thestorage sled 1200 may include additional storage controllers 1220 inother embodiments. The storage controllers 1220 may be embodied as anytype of processor, controller, or control circuit capable of controllingthe storage and retrieval of data into the data storage 1250 based onrequests received via the communication circuit 830. In the illustrativeembodiment, the storage controllers 1220 are embodied as relativelylow-power processors or controllers. For example, in some embodiments,the storage controllers 1220 may be configured to operate at a powerrating of about 75 watts.

In some embodiments, the storage sled 1200 may also include acontroller-to-controller interconnect 1242. Similar to theresource-to-resource interconnect 624 of the sled 400 discussed above,the controller-to-controller interconnect 1242 may be embodied as anytype of communication interconnect capable of facilitatingcontroller-to-controller communications. In the illustrative embodiment,the controller-to-controller interconnect 1242 is embodied as ahigh-speed point-to-point interconnect (e.g., faster than the I/Osubsystem 622). For example, the controller-to-controller interconnect1242 may be embodied as a QuickPath Interconnect (QPI), an UltraPathInterconnect (UPI), or other high-speed point-to-point interconnectdedicated to processor-to-processor communications.

Referring now to FIG. 13, an illustrative embodiment of the storage sled1200 is shown. In the illustrative embodiment, the data storage 1250 isembodied as, or otherwise includes, a storage cage 1252 configured tohouse one or more solid state drives (SSDs) 1254. To do so, the storagecage 1252 includes a number of mounting slots 1256, each of which isconfigured to receive a corresponding solid state drive 1254. Each ofthe mounting slots 1256 includes a number of drive guides 1258 thatcooperate to define an access opening 1260 of the corresponding mountingslot 1256. The storage cage 1252 is secured to the chassis-less circuitboard substrate 602 such that the access openings face away from (i.e.,toward the front of) the chassis-less circuit board substrate 602. Assuch, solid state drives 1254 are accessible while the storage sled 1200is mounted in a corresponding rack 204. For example, a solid state drive1254 may be swapped out of a rack 240 (e.g., via a robot) while thestorage sled 1200 remains mounted in the corresponding rack 240.

The storage cage 1252 illustratively includes sixteen mounting slots1256 and is capable of mounting and storing sixteen solid state drives1254. Of course, the storage cage 1252 may be configured to storeadditional or fewer solid state drives 1254 in other embodiments.Additionally, in the illustrative embodiment, the solid state driversare mounted vertically in the storage cage 1252, but may be mounted inthe storage cage 1252 in a different orientation in other embodiments.Each solid state drive 1254 may be embodied as any type of data storagedevice capable of storing long term data. To do so, the solid statedrives 1254 may include volatile and non-volatile memory devicesdiscussed above.

As shown in FIG. 13, the storage controllers 1220, the communicationcircuit 830, and the optical data connector 834 are illustrativelymounted to the top side 650 of the chassis-less circuit board substrate602. Again, as discussed above, any suitable attachment or mountingtechnology may be used to mount the electrical components of the storagesled 1200 to the chassis-less circuit board substrate 602 including, forexample, sockets (e.g., a processor socket), holders, brackets, solderedconnections, and/or other mounting or securing techniques.

As discussed above, the individual storage controllers 1220 and thecommunication circuit 830 are mounted to the top side 650 of thechassis-less circuit board substrate 602 such that no twoheat-producing, electrical components shadow each other. For example,the storage controllers 1220 and the communication circuit 830 aremounted in corresponding locations on the top side 650 of thechassis-less circuit board substrate 602 such that no two of thoseelectrical components are linearly in-line with other along thedirection of the airflow path 608.

The memory devices 720 of the storage sled 1200 are mounted to thebottom side 750 of the of the chassis-less circuit board substrate 602as discussed above in regard to the sled 400. Although mounted to thebottom side 750, the memory devices 720 are communicatively coupled tothe storage controllers 1220 located on the top side 650 via the I/Osubsystem 622. Again, because the chassis-less circuit board substrate602 is embodied as a double-sided circuit board, the memory devices 720and the storage controllers 1220 may be communicatively coupled by oneor more vias, connectors, or other mechanisms extending through thechassis-less circuit board substrate 602. Each of the storagecontrollers 1220 includes a heatsink 1270 secured thereto. As discussedabove, due to the improved thermal cooling characteristics of thechassis-less circuit board substrate 602 of the storage sled 1200, noneof the heatsinks 1270 include cooling fans attached thereto. That is,each of the heatsinks 1270 is embodied as a fan-less heatsink.

Referring now to FIG. 14, in some embodiments, the sled 400 may beembodied as a memory sled 1400. The storage sled 1400 is optimized, orotherwise configured, to provide other sleds 400 (e.g., compute sleds800, accelerator sleds 1000, etc.) with access to a pool of memory(e.g., in two or more sets 1430, 1432 of memory devices 720) local tothe memory sled 1200. For example, during operation, a compute sled 800or an accelerator sled 1000 may remotely write to and/or read from oneor more of the memory sets 1430, 1432 of the memory sled 1200 using alogical address space that maps to physical addresses in the memory sets1430, 1432. The memory sled 1400 includes various components similar tocomponents of the sled 400 and/or the compute sled 800, which have beenidentified in FIG. 14 using the same reference numbers. The descriptionof such components provided above in regard to FIGS. 6, 7, and 8 applyto the corresponding components of the memory sled 1400 and is notrepeated herein for clarity of the description of the memory sled 1400.

In the illustrative memory sled 1400, the physical resources 620 areembodied as memory controllers 1420. Although only two memorycontrollers 1420 are shown in FIG. 14, it should be appreciated that thememory sled 1400 may include additional memory controllers 1420 in otherembodiments. The memory controllers 1420 may be embodied as any type ofprocessor, controller, or control circuit capable of controlling thewriting and reading of data into the memory sets 1430, 1432 based onrequests received via the communication circuit 830. In the illustrativeembodiment, each storage controller 1220 is connected to a correspondingmemory set 1430, 1432 to write to and read from memory devices 720within the corresponding memory set 1430, 1432 and enforce anypermissions (e.g., read, write, etc.) associated with sled 400 that hassent a request to the memory sled 1400 to perform a memory accessoperation (e.g., read or write).

In some embodiments, the memory sled 1400 may also include acontroller-to-controller interconnect 1442. Similar to theresource-to-resource interconnect 624 of the sled 400 discussed above,the controller-to-controller interconnect 1442 may be embodied as anytype of communication interconnect capable of facilitatingcontroller-to-controller communications. In the illustrative embodiment,the controller-to-controller interconnect 1442 is embodied as ahigh-speed point-to-point interconnect (e.g., faster than the I/Osubsystem 622). For example, the controller-to-controller interconnect1442 may be embodied as a QuickPath Interconnect (QPI), an UltraPathInterconnect (UPI), or other high-speed point-to-point interconnectdedicated to processor-to-processor communications. As such, in someembodiments, a memory controller 1420 may access, through thecontroller-to-controller interconnect 1442, memory that is within thememory set 1432 associated with another memory controller 1420. In someembodiments, a scalable memory controller is made of multiple smallermemory controllers, referred to herein as “chiplets”, on a memory sled(e.g., the memory sled 1400). The chiplets may be interconnected (e.g.,using EMIB (Embedded Multi-Die Interconnect Bridge)). The combinedchiplet memory controller may scale up to a relatively large number ofmemory controllers and I/O ports, (e.g., up to 16 memory channels). Insome embodiments, the memory controllers 1420 may implement a memoryinterleave (e.g., one memory address is mapped to the memory set 1430,the next memory address is mapped to the memory set 1432, and the thirdaddress is mapped to the memory set 1430, etc.). The interleaving may bemanaged within the memory controllers 1420, or from CPU sockets (e.g.,of the compute sled 800) across network links to the memory sets 1430,1432, and may improve the latency associated with performing memoryaccess operations as compared to accessing contiguous memory addressesfrom the same memory device.

Further, in some embodiments, the memory sled 1400 may be connected toone or more other sleds 400 (e.g., in the same rack 240 or an adjacentrack 240) through a waveguide, using the waveguide connector 1480. Inthe illustrative embodiment, the waveguides are 64 millimeter waveguidesthat provide 16 Rx (i.e., receive) lanes and 16 Rt (i.e., transmit)lanes. Each lane, in the illustrative embodiment, is either 16 Ghz or 32Ghz. In other embodiments, the frequencies may be different. Using awaveguide may provide high throughput access to the memory pool (e.g.,the memory sets 1430, 1432) to another sled (e.g., a sled 400 in thesame rack 240 or an adjacent rack 240 as the memory sled 1400) withoutadding to the load on the optical data connector 834.

Referring now to FIG. 15, a system for executing one or more workloads(e.g., applications) may be implemented in accordance with the datacenter 100. In the illustrative embodiment, the system 1510 includes anorchestrator server 1520, which may be embodied as a managed nodecomprising one or more compute devices (e.g., one or more compute sleds800) executing orchestration software (e.g., a cloud operatingenvironment, such as OpenStack) and communicatively coupled to multiplesleds 400 including a large number of compute sleds 1530 (e.g., eachsimilar to the compute sled 800), memory sleds 1540 (e.g., each similarto the memory sled 1400), accelerator sleds 1550 (e.g., each similar tothe memory sled 1000), and storage sleds 1560 (e.g., each similar to thestorage sled 1200). One or more of the sleds 1530, 1540, 1550, 1560 maybe grouped into a managed node 1570, such as by the orchestrator server1520, to collectively perform a workload (e.g., an application 1532executed in a virtual machine or in a container). The managed node 1570may be embodied as an assembly of physical resources 620, such asprocessors 820, memory resources 720, accelerator circuits 1020, or datastorage 1250, from the same or different sleds 400. Further, the managednode may be established, defined, or “spun up” by the orchestratorserver 1520 at the time a workload is to be assigned to the managed nodeor at any other time, and may exist regardless of whether any workloadsare presently assigned to the managed node. In the illustrativeembodiment, the orchestrator server 1520 may selectively allocate and/ordeallocate physical resources 620 from the sleds 400 and/or add orremove one or more sleds 400 from the managed node 1570 as a function ofquality of service (QoS) targets (e.g., performance targets associatedwith a throughput, latency, instructions per second, etc.) associatedwith a service level agreement (SLA) for the workload (e.g., theapplication 1532). In doing so, the orchestrator server 1520 may receivetelemetry data indicative of performance conditions (e.g., throughput,latency, instructions per second, etc.) in each sled 400 of the managednode 1570 and compare the telemetry data to the quality of servicetargets to determine whether the quality of service targets are beingsatisfied. If the so, the orchestrator server 1520 may additionallydetermine whether one or more physical resources may be deallocated fromthe managed node 1570 while still satisfying the QoS targets, therebyfreeing up those physical resources for use in another managed node(e.g., to execute a different workload). Alternatively, if the QoStargets are not presently satisfied, the orchestrator server 1520 maydetermine to dynamically allocate additional physical resources toassist in the execution of the workload (e.g., the application 1532)while the workload is executing

Additionally, in some embodiments, the orchestrator server 1520 mayidentify trends in the resource utilization of the workload (e.g., theapplication 1532), such as by identifying phases of execution (e.g.,time periods in which different operations, each having differentresource utilizations characteristics, are performed) of the workload(e.g., the application 1532) and pre-emptively identifying availableresources in the data center 100 and allocating them to the managed node1570 (e.g., within a predefined time period of the associated phasebeginning). In some embodiments, the orchestrator server 1520 may modelperformance based on various latencies and a distribution scheme toplace workloads among compute sleds and other resources (e.g.,accelerator sleds, memory sleds, storage sleds) in the data center 100.For example, the orchestrator server 1520 may utilize a model thataccounts for the performance of resources on the sleds 400 (e.g., FPGAperformance, memory access latency, etc.) and the performance (e.g.,congestion, latency, bandwidth) of the path through the network to theresource (e.g., FPGA). As such, the orchestrator server 1520 maydetermine which resource(s) should be used with which workloads based onthe total latency associated with each potential resource available inthe data center 100 (e.g., the latency associated with the performanceof the resource itself in addition to the latency associated with thepath through the network between the compute sled executing the workloadand the sled 400 on which the resource is located).

In some embodiments, the orchestrator server 1520 may generate a map ofheat generation in the data center 100 using telemetry data (e.g.,temperatures, fan speeds, etc.) reported from the sleds 400 and allocateresources to managed nodes as a function of the map of heat generationand predicted heat generation associated with different workloads, tomaintain a target temperature and heat distribution in the data center100. Additionally or alternatively, in some embodiments, theorchestrator server 1520 may organize received telemetry data into ahierarchical model that is indicative of a relationship between themanaged nodes (e.g., a spatial relationship such as the physicallocations of the resources of the managed nodes within the data center100 and/or a functional relationship, such as groupings of the managednodes by the customers the managed nodes provide services for, the typesof functions typically performed by the managed nodes, managed nodesthat typically share or exchange workloads among each other, etc.).Based on differences in the physical locations and resources in themanaged nodes, a given workload may exhibit different resourceutilizations (e.g., cause a different internal temperature, use adifferent percentage of processor or memory capacity) across theresources of different managed nodes. The orchestrator server 1520 maydetermine the differences based on the telemetry data stored in thehierarchical model and factor the differences into a prediction offuture resource utilization of a workload if the workload is reassignedfrom one managed node to another managed node, to accurately balanceresource utilization in the data center 100.

To reduce the computational load on the orchestrator server 1520 and thedata transfer load on the network, in some embodiments, the orchestratorserver 1520 may send self-test information to the sleds 400 to enableeach sled 400 to locally (e.g., on the sled 400) determine whethertelemetry data generated by the sled 400 satisfies one or moreconditions (e.g., an available capacity that satisfies a predefinedthreshold, a temperature that satisfies a predefined threshold, etc.).Each sled 400 may then report back a simplified result (e.g., yes or no)to the orchestrator server 1520, which the orchestrator server 1520 mayutilize in determining the allocation of resources to managed nodes.

Referring now to FIG. 16, a system 1610 for providing streamlinedprovisioning of accelerated functions on an as-requested basis (e.g., asa service) includes a pod manager 1620, similar to the orchestratorserver 1520, in communication with multiple sleds 1616, including acompute sled 1630, a memory sled 1640, and an accelerator sled 1650. Inoperation, the compute sled 1630, which is similar to the compute sled1530, executes an application 1632 (e.g., a workload), such as on behalfof a client device 1614. The memory sled 1640, which is similar to thememory sled 1540, includes a memory manager logic unit 1642 and a set ofmemory devices 1644, which together form a pool of memory usable by thesleds on an as-needed basis. The memory manager logic unit 1642 may beembodied as any device or circuitry capable of assigning identifiers(e.g., handles) to data sets to be accessed (e.g., read from and/orwritten to) by other sleds 1616 in the system 1610. The accelerator sled1650, which is similar to the accelerator sled 1550, includes a set ofaccelerator devices 1652, similar to the accelerator circuits 1020described above with reference to FIG. 10. In the illustrativeembodiment, the set of accelerator devices 1652 includes two FPGAs 1654,1656. In other embodiments, the set of accelerator devices 1652 mayinclude any devices or circuitry (e.g., FPGAs, ASICs, GPUs,co-processor(s), etc.) capable of accelerating (e.g., increasing thespeed of execution of) one or more functions. Further, in theillustrative embodiment, each accelerator device 1654, 1656, includes adata manager logic unit 1660, 1662, each of which may be embodied as anydevice or circuitry (e.g., a set of logic gates, a co-processor, anASIC, etc.) capable of streaming (e.g., obtaining on an as-needed basis)data (e.g., input data) from the memory sled 1640 during theacceleration of functions that have been assigned to the acceleratordevice 1654, 1656, and likewise, streaming data (e.g., output data) tothe memory sled 1640 as the data is produced, thereby offloadingcommunication and buffering operations from the components of theaccelerator devices 1654, 1656 that are to perform the acceleration offunctions (e.g., portions of workloads).

In operation, the compute sled 1630 may determine which compute device(e.g., the pod manager 1620) in the system 1610 is providing anacceleration scheduling service (e.g., a service to assign functions toaccelerator devices). The compute sled 1630 may also send a data set tobe used as input to the accelerated function into the memory pool of thememory sled 1640, receive a service identifier (e.g., a handle) for thewritten data set, and send a request for acceleration to the computedevice (e.g., the pod manager 1620) providing the accelerationscheduling service. The request, in the illustrative embodiment,includes the service identifier and descriptor data indicative of thetype of function to be accelerated and a performance threshold to besatisfied (e.g., a reference to a service level agreement (SLA), aquality of service, a latency, etc.). The compute device providing theacceleration scheduling service (e.g., the pod manager 1620) matches thedescriptor data to the available accelerator devices 1652 to select oneor more of the accelerator devices 1652 to perform acceleration of thefunction, and sends a request to the accelerator device 1652 (e.g., tothe corresponding accelerator sled 1650) with the service identifier.The accelerator device 1652 then obtains the input data set from thememory sled, operates on the input data set (e.g., by executing theaccelerated function) to produce a resultant data set and writes theresultant data set to the memory pool in association with the serviceidentifier. The pod manager 1620 then notifies the compute sled 1630that the acceleration has been completed, and the compute sled 1630reads the resultant data set from the memory pool (e.g., by sending aread request to the memory sled 1640 with the service identifier andreceiving the corresponding resultant data). As such, the system 1610enables an intermediary compute device (e.g., the pod manager 1620) tomatch compute sleds with accelerator devices on an as-requested basiswhile avoiding the network communication load that would otherwiseresult from the intermediary compute device (e.g., the pod manager 1620)receiving and sending data sets between the sleds 1616. Accordingly, thesystem 1610 may execute workloads more efficiently than known systems.

As described above, the pod manager 1620, the sleds 1616, and the clientdevice 1614 are illustratively in communication via the network 1612,which may be embodied as any type of wired or wireless communicationnetwork, including global networks (e.g., the Internet), local areanetworks (LANs) or wide area networks (WANs), cellular networks (e.g.,Global System for Mobile Communications (GSM), 3G, Long Term Evolution(LTE), Worldwide Interoperability for Microwave Access (WiMAX), etc.),digital subscriber line (DSL) networks, cable networks (e.g., coaxialnetworks, fiber networks, etc.), or any combination thereof.

Referring now to FIG. 17, the compute sled 1630, in operation, mayexecute a method 1700 for utilizing streamlined provisioning ofaccelerated functions. The method 1700 begins with block 1702, in whichthe compute sled 1630 executes a workload (e.g., the application 1632).As indicated in block 1704, the compute sled 1630 may receive acompletion notification (e.g., from the pod manager 1620) indicating theacceleration of a function has been completed. Initially, the computesled 1630 may not receive a completion notification, as it has not yetrequested acceleration of any functions. In response to a determinationthat a completion notification has not been received, the method 1700advances to block 1706, in which the compute sled 1630 determineswhether to accelerate one or more functions of the workload 1632. Indoing so, the compute sled 1630 may determine that a function in theworkload has been flagged (e.g., in metadata associated with theworkload) as being amenable to acceleration (e.g., would execute moreefficiently on an accelerator device having a different architecturethan the processor 820 of the compute sled 1630) or that the function,when executed previously by the processor 820, did not satisfy apredefined target performance threshold (e.g., an expected amount oftime to complete the function). In other embodiments, the compute sled1630 may determine whether to request acceleration of a function basedon other factors. Regardless, in response to a determination toaccelerate the function, the method 1700 advances to block 1708, inwhich the compute sled 1630 sends, to the memory sled 1640, data (e.g.,an input data set) on which the accelerated function is to operate. Forexample, the compute sled 1630 may send a set of data to be compressed,encrypted, or otherwise operated on, to the memory sled 1640, asdescribed in more detail herein. Subsequently, in block 1710, thecompute sled 1630 receives, from the memory sled 1640, a serviceidentifier indicative of a handle for the data set to be operated on(e.g., the data set that was sent in block 1708). In the illustrativeembodiment, the service identifier is a handle that is memory locationindependent, meaning the service identifier is level of indirection awayfrom the actual location (e.g., physical or logical address) of wherethe data resides in the memory sled 1640, thereby allowing the memorysled 1640 to relocate, resize, overwrite, or otherwise modify the dataassociated with the acceleration of the function in the memory 1644, asneeded, without affecting the service identifier provided to the computesled 1630.

Afterwards, in block 1712, the compute sled 1630, sends a request to acompute device (e.g., the pod manager 1620) to schedule acceleration ofthe function. In doing so, and as indicated in block 1714, the computesled 1630, in the illustrative embodiment, identifies a compute deviceoperating as an acceleration scheduler (e.g., will match a request foracceleration with one or more accelerator devices 1652 capable ofperforming the acceleration). To do so, the compute sled 1630 mayidentify a compute device operating as an acceleration scheduler inresponse to receiving a multicast notification (e.g., a communicationsent to multiple sleds) from the compute device that indicates that thecompute device is operating as an acceleration scheduler. Alternatively,the compute sled 1630 may affirmatively query the compute device andreceive a response indicating that the compute device is operating as anacceleration scheduler, as indicated in block 1716. Additionally oralternatively, the compute sled 1630 may obtain a predefined identifierof the compute device (e.g., from a configuration file), as indicated inblock 1718. In the illustrative embodiment, the compute sled 1630identifies the pod manager 1620 as the compute device operating as theacceleration scheduler, as indicated in block 1718.

The compute sled 1630, in the illustrative embodiment, sends the serviceidentifier, obtained from the memory sled 1640 in block 1710, as aparameter of the request for acceleration, as indicated in block 1722.Additionally, as indicated in block 1724, the compute sled 1630 sendsdescriptor data, which may be embodied any data indicative of thetype(s) (e.g., compression, encryption, etc.) of function(s) to beaccelerated and one or more performance targets to be satisfied. Indoing so, the compute sled 1630 may send descriptor data indicative of aservice level agreement (SLA) to be satisfied, as indicated in block1726 and/or may send descriptor data indicative of a latency thresholdto be satisfied (e.g., that the acceleration of the function(s) are tobe completed within a predefined amount of time), as indicated in block1728. Subsequently, the method 1700 loops back to block 1702, in whichthe compute sled 1630 continues execution of the workload. Returning toblock 1704, if the compute sled 1630 receives a completion notification(e.g., from the pod manager 1620 or other compute device that isoperating as the acceleration scheduler), the method 1700 advances toblock 1730. In the illustrative embodiment, the completion notificationincludes the service identifier and, in block 1730, the compute sled1630 obtains (e.g., parses) the service identifier from the completionnotification. In block 1732, the compute sled 1630 obtains, using theservice identifier, the resultant data (e.g., the output data producedby the accelerator device(s) that performed the accelerated functionassociated with the acceleration request) from the memory sled 1640. Indoing so, in the illustrative embodiment, the compute sled 1630 sends aread request that includes the service identifier (e.g., from block1730) to the memory sled 1640, as indicated in block 1734. Further, asindicated in block 1736, the compute sled 1630 receives the resultantdata from the memory sled 1640 and copies it to local memory of thecompute sled 1630 (e.g., the memory resources 720 of FIG. 7).Subsequently, the method 1700 advances to block 1706 to again determinewhether to request acceleration of any functions in the workload.

Referring now to FIG. 18, the memory sled 1640, in operation, mayexecute a method 1800 for facilitating streamlined provisioning ofaccelerated functions in the system 1610. The method 1800 begins withblock 1802, in which the memory sled 1640 determines whether a memoryaccess request has been received. In response to a determination that amemory access request has been received, the method 1800 advances toblock 1804 in which the memory sled 1640 determines the subsequentcourse of action as a function of whether the memory access request is awrite request. If the memory access request is a write request, themethod 1800 advances to block 1806, in which the memory sled 1640determines whether a service identifier is included in the writerequest. If not, the method 1800 advances to block 1808 in which thememory sled 1640 produces a service identifier (e.g., as a function of apresent time stamp, an identifier of the requesting compute sled 1630, ahash of the data to be written, and/or other factors). Further, in block1810, the memory sled 1640 associates memory locations (e.g., physicalor logical addresses where the data is to be written) with the serviceidentifier, such as in a memory map. Additionally, the memory sled 1640writes the data from the memory access request to the associated memorylocations, as indicated in block 1812. Subsequently, in block 1814, thememory sled 1640 sends the service identifier to the requesting sled(e.g., the compute sled 1630). Referring back to block 1806, if theservice identifier was included in the write request, the method 1800instead advances to block 1816, in which the memory sled 1640 writesdata from the request to one or more memory locations associated withthe service identifier. In either case, the method 1800 loops back toblock 1802 to await another memory access request.

Returning to block 1804, if the memory sled 1640 instead determines thatthe memory access request is not a write request (e.g., is a readrequest), the method 1800 advances to block 1818, in which the memorysled 1640 obtains the service identifier from the memory access request.Further, in block 1820, the memory sled 1640 determines the memorylocation(s) associated with the service identifier (e.g., from theassociation produced in block 1810). In block 1822, the memory sled 1640reads data from the determined memory location(s) and in block 1824, thememory sled 1640 sends the read data to the requesting sled (e.g., thecompute sled 1630, the accelerator sled 1650, etc.). In doing so, in theillustrative embodiment, the memory sled 1640 sends the serviceidentifier with the read data, as indicated in block 1826. Subsequently,the method 1800 loops back to block 1802 to await another memory accessrequest.

Referring now to FIG. 19, the accelerator 1650, in operation, mayperform a method 1900 for accelerating a function on an as-requestedbasis. The method 1900 begins with block 1902, in which the acceleratorsled 1650 determines whether a request (e.g., from the pod manager 1620or other compute device operating as an acceleration scheduler) toassign acceleration of a function to one or more identified acceleratordevices 1652 of the accelerator sled 1650 has been received. If so, themethod 1900 advances to block 1904, in which the accelerator sled 1650obtains (e.g., parses) the service identifier from the request.Subsequently, in block 1906, the accelerator sled 1650 (e.g., with thedata manager logic unit 1660 of the FPGA 1654) sends a request,including the service identifier, to the memory sled 1640. In block1908, the accelerator sled 1650 receives input data from the memory sled(e.g., the data provided by the compute sled in block 1708 of FIG. 17).In doing so, in the illustrative embodiment, the accelerator sled 1650streams (e.g., obtains subsets of the input data on an as-needed basis)the input data from the memory sled 1640, as indicated in block 1910. Asindicated in block 1912, the accelerator sled 1650 executes (e.g., withthe associated accelerator device 1652, such as the FPGA 1654) theaccelerated function to produce resultant data (e.g., output data).Further, and as indicated in block 1914, the accelerator sled 1650 sendsthe resultant data to the memory sled 1640 using a write request. Indoing so, in the illustrative embodiment, the accelerator sled 1650streams the resultant data to the memory sled 1640 as the resultant datais produced (e.g., rather than waiting until execution of the functionhas completed), as indicated in block 1916. As indicated in block 1918,the accelerator sled 1650 sends the service identifier (e.g., from block1904) with the resultant data to the memory sled 1640. In block 1920,the accelerator sled 1650 determines whether acceleration of thefunction is complete. If not, the method 1900 loops back to block 1908to continue to receive input data and accelerate the function.Otherwise, the method 1900 advances to block 1922, in which theaccelerator sled 1650 sends a completion notification to the assigningcompute device (e.g., the compute device that sent the assignmentrequest from block 1902). In doing so, in the illustrative embodiment,the accelerator sled 1650 sends the completion notification to the podmanager 1620. Subsequently, the method 1900 loops back to block 1902 toawait another assignment request.

Referring now to FIG. 20, the pod manager 1620, in operation, mayexecute a method 2000 for coordinating the provisioning of acceleratedfunctions on an as-requested basis (e.g., as a service). The method 2000begins with block 2002 in which the pod manager 1620 receivesaccelerator device data indicative of the present status of acceleratordevices (e.g., the accelerator devices 1652) on one or more acceleratorsleds 1650. In doing so, the pod manager 1620, in the illustrativeembodiment, receives capacity data indicative of an accelerationcapacity (e.g., number of idle FPGA slots, present load, etc.) of eachaccelerator device 1652, as indicated in block 2004. Additionally, inthe illustrative embodiment, the pod manager 1620 receives acceleratorkernel data indicative of function(s) that each accelerator device ispresently configured to accelerate (e.g., identifiers of function names,identifiers of types of functions such as compression, encryption,etc.), as indicated in block 2006. Subsequently, in block 2008, the podmanager 1620 determines whether an acceleration request has beenreceived (e.g., from the compute sled 1630). If not, the method 2000loops back to block 2002 in which the pod manager 1620 receives updatedaccelerator device data. Otherwise, the method 2000 advances to block2010 in which the pod manager 1620 determines the type(s) of function(s)to be accelerated and performance target(s) from descriptor dataincluded in the request (e.g., the descriptor data from block 1724 ofFIG. 17). In block 2012, the pod manager 1620 determines one or moreaccelerator devices to accelerate the function(s). In doing so, the podmanager 1620, in the illustrative embodiment, matches the performancetarget(s) to the capacities of the accelerator devices 1652, asindicated in block 2014. In doing so, the pod manager 1620 may select anaccelerator device 1652 having a larger capacity for a performancetarget that is relatively more demanding (e.g., a lower latency target)or, conversely, select an accelerator device 1652 having less capacityfor a performance target that is relatively less demanding. As indicatedin block 2016, the pod manager 1620 may match the type(s) of thefunction(s) to be accelerated to the received kernel data from block2006 (e.g., to match a request to accelerate a compression function withan FPGA having a kernel configured to perform a compression function).In some embodiments, the pod manager 1620 may determine a sequence inwhich the multiple functions associated with the request are to beperformed (e.g., based on the data dependence of each function, based ona sequence defined in the descriptor data, etc.) and the acceleratordevices that are to perform each function, as indicated in block 2018.Subsequently, the method 2000 advances to block 2020 of FIG. 21, inwhich the pod manager 1620 sends, to the determined acceleratordevice(s) (e.g., to the corresponding accelerator sled 1650), request(s)to accelerate a corresponding function.

Referring now to FIG. 21, in sending the request(s), the pod manager1620, in the illustrative embodiment, sends the service identifier withthe request, as indicated in block 2022. In block 2024, the pod manager1620 determines whether a completion notification has been received fromthe accelerator device(s) 1652. If not, the pod manager 1620 continuesto await a completion notification. Otherwise, if the pod manager 1620receives a completion notification, the method 2000 advances to block2026 in which the pod manager 1620 determines whether another functionexists in a sequence of functions associated with the accelerationrequest. If so, the method 2000 advances to block 2028, in which the podmanager 1620 selects the next function in the sequence, and the method2000 loops back to block 2020, in which the pod manager 1620 requeststhe corresponding accelerator device(s) to accelerate the function.Otherwise, the method 2000 instead advances to block 2030, in which thepod manager 1620 sends a notification of completion to the requestingsled (e.g., the compute sled 1630). In doing so, the pod manager 1620sends the service identifier with the notification, as indicated inblock 2032. Subsequently, the method 2000 loops back to block 2002 ofFIG. 20, in which the pod manager 1620 receives updated acceleratordevice data, as described above.

EXAMPLES

Illustrative examples of the technologies disclosed herein are providedbelow. An embodiment of the technologies may include any one or more,and any combination of, the examples described below.

Example 1 includes a compute sled comprising a network interfacecontroller; and circuitry to (i) determine whether to accelerate afunction of a workload executed by the compute sled; (ii) send, to amemory sled and in response to a determination to accelerate thefunction, a data set on which the function is to operate; (iii) receive,from the memory sled, a service identifier indicative of a memorylocation independent handle for data associated with the function; (iv)send, to a compute device, a request to schedule acceleration of thefunction on the data set; (v) receive a notification of completion ofthe acceleration of the function; and (vi) obtain, in response toreceipt of the notification and using the service identifier, aresultant data set from the memory sled, wherein the resultant data setwas produced by an accelerator device during acceleration of thefunction on the data set.

Example 2 includes the subject matter of Example 1, and wherein toobtain the resultant data set from the memory sled comprises to send,with the network interface controller, a read a request with the serviceidentifier to the memory sled; receive, with the network interfacecontroller, the resultant data set from the memory sled after the readrequest has been sent; and copy the resultant data set to a local memoryof the compute sled.

Example 3 includes the subject matter of any of Examples 1 and 2, andwherein the circuitry is further to determine whether the compute deviceis to operate as an acceleration scheduler and wherein to send therequest to schedule acceleration of the function comprises to send, tothe compute device, the request in response to a determination that thecompute device is to operate as an acceleration scheduler.

Example 4 includes the subject matter of any of Examples 1-3, andwherein the network interface controller is to receive a multicastnotification from the compute device that identifies the compute deviceas an acceleration scheduler and wherein to determine whether thecompute device is to operate as an acceleration scheduler comprises todetermine, in response to receipt of the multicast notification, thatthe compute device is to operate as an acceleration scheduler.

Example 5 includes the subject matter of any of Examples 1-4, andwherein the network interface controller is to send a query to thecompute device to determine whether the compute device is to operate asthe acceleration scheduler; and receive a response from the computedevice that acknowledges that the compute device is to operate as theacceleration scheduler.

Example 6 includes the subject matter of any of Examples 1-5, andwherein to send, to a compute device, a request to schedule accelerationof the function on the data set comprises to send the request to a podmanager.

Example 7 includes the subject matter of any of Examples 1-6, andwherein to send the request comprises to send descriptor data indicativeof a type of function to be accelerated.

Example 8 includes the subject matter of any of Examples 1-7, andwherein to send the request comprises to send descriptor data that isfurther indicative of a performance target to be satisfied.

Example 9 includes the subject matter of any of Examples 1-8, andwherein to send descriptor data that is further indicative of aperformance target to be satisfied comprises to send data indicative ofa service level agreement to be satisfied.

Example 10 includes the subject matter of any of Examples 1-9, andwherein to send descriptor data that is further indicative of aperformance target to be satisfied comprises to send data indicative ofa latency threshold to be satisfied.

Example 11 includes one or more machine-readable storage mediacomprising a plurality of instructions stored thereon that, in responseto being executed, cause a compute sled to determine whether toaccelerate a function of a workload executed by the compute sled; send,to a memory sled and in response to a determination to accelerate thefunction, a data set on which the function is to operate; receive, fromthe memory sled, a service identifier indicative of a memory locationindependent handle for data associated with the function; send, to acompute device, a request to schedule acceleration of the function onthe data set; receive a notification of completion of the accelerationof the function; and obtain, in response to receipt of the notificationand using the service identifier, a resultant data set from the memorysled, wherein the resultant data set was produced by an acceleratordevice during acceleration of the function on the data set.

Example 12 includes the subject matter of Example 11, and wherein toobtain the resultant data set from the memory sled comprises to send,with the network interface controller, a read a request with the serviceidentifier to the memory sled; receive, with the network interfacecontroller, the resultant data set from the memory sled after the readrequest has been sent; and copy the resultant data set to a local memoryof the compute sled.

Example 13 includes the subject matter of any of Examples 11 and 12, andwherein, when executed, the plurality of instructions further cause thecompute sled to determine whether the compute device is to operate as anacceleration scheduler and wherein to send the request to scheduleacceleration of the function comprises to send, to the compute device,the request in response to a determination that the compute device is tooperate as an acceleration scheduler.

Example 14 includes the subject matter of any of Examples 11-13, andwherein, when executed, the plurality of instructions further cause thecompute sled to receive a multicast notification from the compute devicethat identifies the compute device as an acceleration scheduler andwherein to determine whether the compute device is to operate as anacceleration scheduler comprises to determine, in response to receipt ofthe multicast notification, that the compute device is to operate as anacceleration scheduler.

Example 15 includes the subject matter of any of Examples 11-14, andwherein, when executed, the plurality of instructions further cause thecompute sled to send a query to the compute device to determine whetherthe compute device is to operate as the acceleration scheduler; andreceive a response from the compute device that acknowledges that thecompute device is to operate as the acceleration scheduler.

Example 16 includes the subject matter of any of Examples 11-15, andwherein to send, to a compute device, a request to schedule accelerationof the function on the data set comprises to send the request to a podmanager.

Example 17 includes the subject matter of any of Examples 11-16, andwherein to send the request comprises to send descriptor data indicativeof a type of function to be accelerated.

Example 18 includes the subject matter of any of Examples 11-17, andwherein to send the request comprises to send descriptor data that isfurther indicative of a performance target to be satisfied.

Example 19 includes the subject matter of any of Examples 11-18, andwherein to send descriptor data that is further indicative of aperformance target to be satisfied comprises to send data indicative ofa service level agreement to be satisfied.

Example 20 includes the subject matter of any of Examples 11-19, andwherein to send descriptor data that is further indicative of aperformance target to be satisfied comprises to send data indicative ofa latency threshold to be satisfied.

Example 21 includes a compute device comprising circuitry fordetermining whether to accelerate a function of a workload executed bythe compute sled; circuitry for sending, to a memory sled and inresponse to a determination to accelerate the function, a data set onwhich the function is to operate; circuitry for receiving, from thememory sled, a service identifier indicative of a memory locationindependent handle for data associated with the function; means forsending, to a compute device, a request to schedule acceleration of thefunction on the data set; circuitry for receiving a notification ofcompletion of the acceleration of the function; and circuitry forobtaining, in response to receipt of the notification and using theservice identifier, a resultant data set from the memory sled, whereinthe resultant data set was produced by an accelerator device duringacceleration of the function on the data set.

Example 22 includes a method comprising determining, by a compute sled,whether to accelerate a function of a workload executed by the computesled; sending, by the compute sled and to a memory sled, and in responseto a determination to accelerate the function, a data set on which thefunction is to operate; receiving, by the compute sled and from thememory sled, a service identifier indicative of a memory locationindependent handle for data associated with the function; sending, bythe compute sled and to a compute device, a request to scheduleacceleration of the function on the data set; receiving, by the computesled, a notification of completion of the acceleration of the function;and obtaining, by the compute sled and in response to receipt of thenotification, and using the service identifier, a resultant data setfrom the memory sled, wherein the resultant data set was produced by anaccelerator device during acceleration of the function on the data set.

Example 23 includes the subject matter of Example 22, and whereinobtaining the resultant data set from the memory sled comprises sending,with a network interface controller of the compute sled, a read requestwith the service identifier to the memory sled; receiving, with thenetwork interface controller of the compute sled, the resultant data setfrom the memory sled after the read request has been sent; and copying,by the compute sled, the resultant data set to a local memory of thecompute sled.

Example 24 includes the subject matter of any of Examples 22 and 23, andfurther including determining, by the compute sled, whether the computedevice is to operate as an acceleration scheduler and wherein sendingthe request to schedule acceleration of the function comprises sending,to the compute device, the request in response to a determination thatthe compute device is to operate as an acceleration scheduler.

Example 25 includes the subject matter of any of Examples 22-24, andfurther including receiving, by the compute sled, a multicastnotification from the compute device that identifies the compute deviceas an acceleration scheduler and wherein determining whether the computedevice is to operate as an acceleration scheduler comprises todetermine, in response to receipt of the multicast notification, thatthe compute device is to operate as an acceleration scheduler.

The invention claimed is:
 1. An apparatus comprising: a networkinterface controller; and circuitry to: (i) determine whether toaccelerate a function of a workload to be executed; (ii) send, to amemory device and in response to a determination to accelerate thefunction, a data set on which the function is to operate; (iii) receive,from the memory device, a service identifier indicative of a memorylocation associated with data of the function; (iv) send, to a computedevice, a request to schedule acceleration of the function on the dataset; (v) receive a notification of completion of the acceleration of thefunction; and (vi) obtain, in response to receipt of the notificationand using the service identifier, a resultant data set from the memorydevice, the resultant data set to be produced by an accelerator deviceduring acceleration of the function on the data set, wherein to obtainthe resultant data set from the memory device includes: the networkinterface controller to send a read request with the service identifierto the memory device; the network interface controller to receive theresultant data set from the memory device after the read request hasbeen sent; and copy the resultant data set to a local memory.
 2. Theapparatus of claim 1, wherein the circuitry is further to determinewhether the compute device is to operate as an acceleration schedulerand wherein to send the request to schedule acceleration of the functioncomprises to send, to the compute device, the request in response to adetermination that the compute device is to operate as an accelerationscheduler.
 3. The apparatus of claim 2, wherein the network interfacecontroller is to receive a multicast notification from the computedevice that identifies the compute device as an acceleration schedulerand wherein to determine whether the compute device is to operate as anacceleration scheduler comprises to determine, in response to receipt ofthe multicast notification, that the compute device is to operate as anacceleration scheduler.
 4. The apparatus of claim 2, wherein the networkinterface controller is to: send a query to the compute device todetermine whether the compute device is to operate as the accelerationscheduler; and receive a response from the compute device thatacknowledges that the compute device is to operate as the accelerationscheduler.
 5. The apparatus of claim 1, wherein to send, to a computedevice, a request to schedule acceleration of the function on the dataset comprises to send the request to a pod manager.
 6. The apparatus ofclaim 1, wherein to send the request comprises to send descriptor dataindicative of a type of function to be accelerated.
 7. The apparatus ofclaim 6, wherein to send the request comprises to send descriptor datathat is further indicative of a performance target to be satisfied. 8.The apparatus of claim 7, wherein to send descriptor data that isfurther indicative of a performance target to be satisfied comprises tosend data indicative of a service level agreement to be satisfied. 9.The apparatus of claim 7, wherein to send descriptor data that isfurther indicative of a performance target to be satisfied comprises tosend data indicative of a latency threshold to be satisfied.
 10. One ormore non-transitory machine-readable storage media comprising aplurality of instructions stored thereon that, in response to beingexecuted, cause a compute resource to: determine whether to accelerate afunction of a workload to be executed; send, to a memory device and inresponse to a determination to accelerate the function, a data set onwhich the function is to operate; receive, from the memory device, aservice identifier indicative of a memory location associated with dataof the function; send, to a compute device, a request to scheduleacceleration of the function on the data set; receive a notification ofcompletion of the acceleration of the function; and obtain, in responseto receipt of the notification and using the service identifier, aresultant data set from the memory device, the resultant data set to beproduced by an accelerator device during acceleration of the function onthe data set, wherein to obtain the resultant data set from the memorydevice includes the compute resource to: send a read request with theservice identifier to the memory device; receive the resultant data setfrom the memory device after the read request has been sent; and copythe resultant data set to a local memory.
 11. The one or morenon-transitory machine-readable storage media of claim 10, wherein, whenexecuted, the plurality of instructions further cause the computeresource to determine whether the compute device is to operate as anacceleration scheduler and wherein to send the request to scheduleacceleration of the function comprises to send, to the compute device,the request in response to a determination that the compute device is tooperate as an acceleration scheduler.
 12. The one or more non-transitorymachine-readable storage media of claim 11, wherein, when executed, theplurality of instructions further cause the compute resource to receivea multicast notification from the compute device that identifies thecompute device as an acceleration scheduler and wherein to determinewhether the compute device is to operate as an acceleration schedulercomprises to determine, in response to receipt of the multicastnotification, that the compute device is to operate as an accelerationscheduler.
 13. The one or more non-transitory machine-readable storagemedia of claim 11, wherein, when executed, the plurality of instructionsfurther cause the compute resource to: send a query to the computedevice to determine whether the compute device is to operate as theacceleration scheduler; and receive a response from the compute devicethat acknowledges that the compute device is to operate as theacceleration scheduler.
 14. The one or more non-transitorymachine-readable storage media of claim 10, wherein to send, to acompute device, a request to schedule acceleration of the function onthe data set comprises to send the request to a pod manager.
 15. The oneor more non-transitory machine-readable storage media of claim 10,wherein to send the request comprises to send descriptor data indicativeof a type of function to be accelerated.
 16. The one or morenon-transitory machine-readable storage media of claim 15, wherein tosend the request comprises to send descriptor data that is furtherindicative of a performance target to be satisfied.
 17. The one or morenon-transitory machine-readable storage media of claim 16, wherein tosend descriptor data that is further indicative of a performance targetto be satisfied comprises to send data indicative of a service levelagreement to be satisfied.
 18. The one or more non-transitorymachine-readable storage media of claim 16, wherein to send descriptordata that is further indicative of a performance target to be satisfiedcomprises to send data indicative of a latency threshold to besatisfied.
 19. An apparatus comprising: circuitry for determiningwhether to accelerate a function of a workload to be executed; circuitryfor sending, to a memory device and in response to a determination toaccelerate the function, a data set on which the function is to operate;circuitry for receiving, from the memory device, a service identifierindicative of a memory location associated with data of the function;circuitry for sending, to a compute device, a request to scheduleacceleration of the function on the data set; circuitry for receiving anotification of completion of the acceleration of the function; andcircuitry for obtaining, in response to receipt of the notification andusing the service identifier, a resultant data set from the memorydevice, the resultant data set to be produced by an accelerator deviceduring acceleration of the function on the data set, wherein obtainingthe resultant data set from the memory device includes: sending a readrequest with the service identifier to the memory device; receiving theresultant data set from the memory device after the read request hasbeen sent; and copying the resultant data set to a local memory.
 20. Amethod comprising: determining, by a compute resource, whether toaccelerate a function of a workload to be executed; sending, by thecompute resource and to a memory device, and in response to adetermination to accelerate the function, a data set on which thefunction is to operate; receiving, by the compute resource and from thememory device, a service identifier indicative of a memory locationassociated with data of the function; sending, by the compute resourceand to a compute device, a request to schedule acceleration of thefunction on the data set; receiving, by the compute resource, anotification of completion of the acceleration of the function; andobtaining, by the compute resource and in response to receipt of thenotification, and using the service identifier, a resultant data setfrom the memory device the resultant data set to be produced by anaccelerator device during acceleration of the function on the data set,wherein obtaining the resultant data set from the memory deviceincludes: sending a read request with the service identifier to thememory device; receiving the resultant data set from the memory deviceafter the read request has been sent; and copying the resultant data setto a local memory of the compute resource.
 21. The method of claim 20,further comprising determining, by the compute resource, whether thecompute device is to operate as an acceleration scheduler and whereinsending the request to schedule acceleration of the function comprisessending, to the compute device, the request in response to adetermination that the compute device is to operate as an accelerationscheduler.
 22. The method of claim 21, further comprising receiving, bythe compute resource, a multicast notification from the compute devicethat identifies the compute device as an acceleration scheduler andwherein determining whether the compute device is to operate as anacceleration scheduler comprises to determine, in response to receipt ofthe multicast notification, that the compute device is to operate as anacceleration scheduler.